Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices
Finding the optimal cluster number and validating the partition resultsof a data set are difficult tasks since clustering is an unsupervised learning process.Cluster validity index (CVI) is a kind of criterion function for evaluating the clusteringresults and determining the optimal number of cluste...
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| Veröffentlicht in: | International journal of computers, communications & control communications & control, 2014-06, Vol.9 (3), p.370 |
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| container_title | International journal of computers, communications & control |
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| creator | Zhou, Kaile Ding, Shuai Fu, Chao Yang, Shanlin |
| description | Finding the optimal cluster number and validating the partition resultsof a data set are difficult tasks since clustering is an unsupervised learning process.Cluster validity index (CVI) is a kind of criterion function for evaluating the clusteringresults and determining the optimal number of clusters. In this paper, we present anextensive comparison of ten well-known CVIs for fuzzy clustering. Then we extendtraditional single CVIs by introducing the weighted method and propose a weightedsummation type of CVI (WSCVI). Experiments on nine synthetic data sets and fourreal-world UCI data sets demonstrate that no one CVI performs better on all datasets than others. Nevertheless, the proposed WSCVI is more effective by properlysetting the weights. |
| doi_str_mv | 10.15837/ijccc.2014.3.237 |
| format | Article |
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| ispartof | International journal of computers, communications & control, 2014-06, Vol.9 (3), p.370 |
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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Clustering Datasets |
| title | Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices |
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